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Measuring the Efficiency and Productivity Change of APEC Mobile Telecommunications Firm

Ya-Ting Chao1 Abstract

The Asia-Pacific Economic Cooperation (APEC) mobile operators play an influential and fundamental role in global telecommunications industry and show pretty well performances both in penetration and growth of mobile subscribers. This study is to measure the efficiency and productivity change of 28 APEC’s mobile operators during the time period of 2003 to 2008, using the DEA and Malmquist index approaches. Two output variables are operating revenue and number of mobile subscribers, and three input variables are number of employees, total assets and capital expenditures. The empirical results of the DEA model show that three operators, KDDI, Telkomsel and Smart Communication, were fully efficient with all the values of TE, PTE and SE equal to 1. But, Telstra, Rogers Wireless, Bell Wireless, Verizon Wireless and AT&T Mobility were the ones with the technical efficiency of less than 0.6 on average. It is found that operators with large revenues do not necessarily achieve high efficiency. In particular, these operators, as the leading role in the telecommunication industry, have to develop pioneering technologies on services and applications and provide new network systems ahead of their rivals. Therefore, these might bring the inefficiency to large operators. Next, the results of Malmquist productivity index show that productivity increased by 5.5% between 2003 and 2008 or about 1.1% per year. This growth is due primarily to improvements in technical efficiency rather than innovation.

Keywords: Efficiency, Productivity change, APEC mobile operator

1. Introduction 1.1 Background and motivation

Productive efficiency is a measure relating a quantity or quality of output to the inputs required to produce it. In nowadays competitive environment, measuring productive efficiency helps a firm or an organization to know how to improve its capability in the process of producing and to find the way to use its resources and inputs more efficiently. In addition, the productivity measures are generally regarded as a more reliable indicator of industry performance than profitability (Madden and Savage, 1999). Extensive researches have measure the productivity and efficiency of firms in diverse fields. In particular, the liberalization and privatization in global telecommunications markets in the last two decades have attracted academician attention on the productive efficiency in telecommunications.

Various methodologies have been used to measure the efficiency and productivity change, including the conventional growth-accounting approach, total factor productivity (TFP) measurement, the Divisia aggregation method, the Malmquist index of TFP, data envelopment analysis (DEA), and other measurements. For instance, Calabrese, Campisi and Mancuso (2002) examined the productivity growth in the telecommunications industries of 13 OECD countries during 1979 to 1988 by the Malmquist TFP index, revealing that technical change was the most

1 Institute of Telecommunications Management, National Cheng Kung University, Tainan 70101, Taiwan (E-mail: vivian0326@yahoo.com.tw).

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important factor for the TFP growth. Uri (2000) examined the productivity growth of 19 local exchange carriers (LECs) in the United States during 1988 to 1998 by the growth accounting and Malmquist index, and concluded that the productivity growth was primarily due to the innovation rather than the improvements in efficiency. Tsai, Chen and Tzeng (2006) adopted traditional DEA, A&P efficiency measure and efficiency achievement measure to discover the productivity ranking of 39 leading telecommunication operators in Forbes 2000. The results indicated that Asia-Pacific telecom operators have better productivity efficiency than those in Europe and America.

The Asia-Pacific Economic Cooperation (APEC) has a great influence on the world’s economical growth and development. APEC’s mobile operators play an influential and fundamental role in global telecommunication industry. Mobile operators currently face fierce challenges from different industries and international competition. For instance, entering WTO is a significant step towards the further development and reform of a country’s mobile market. The commitments to join the WTO have rendered investment environment more suitable for international investments in this sensitive field. The restrictions of foreign-capital investment on telecommunication operators have been lifted due to the WTO's protocol. Foreign mobile operators bring positive effects of raising funds and equipment/technology upgrade in these countries by entering the domestic market.2 In addition, the merger and alliance between operators enhance the business competitiveness. Accordingly, mobile operators are able to upgrade the telecommunications systems and provide better services. On the other hand, to pursue a faster bandwidth and full coverage, a new generation of mobile systems has a much shorter life cycle. In sum, mobile operators bear higher infrastructure costs and mobile market becomes increasingly competitive. Therefore, to find a suitable way to measure the operator's the efficiency and productivity change is thus important.

1.2 Development of mobile telecommunications in APEC Global economy stably developed from 2004 to 2008 with a growth rate of

around 4.0%. However, the subprime mortgage crisis from the U.S. seriously struck various countries resulting in global financial downturn. The emerging economies in Asia, especially China, India and Russia, were still strong with high growth rates and played the role of a driving engine for the global economy. APEC, established in 1989, is the premier forum for promoting economic growth, cooperation, trade and investment in the Asia Pacific region. The 21 members in APEC, accounted for 40.5% of the world's population, approximately have 54.2% (28.6 trillion) of world gross domestic product (GDP) and 43.7% of world trade volume in 2007 (APEC, 2008). The average economic growth rate from 2000 to 2007 in the APEC was 4.71%, higher than the global value of 3.2% (The World Bank, 2009).

According to International Telecommunication Union (ITU, 2009), the number of global mobile subscribers has reached to 4 billion in 2008, with the penetration rate of 59.34 percent in the world's population of 6.77 billion. It reveals that mobile services significantly affect human being’s life and technology, and bring enormous

2 Take Vietnam as an example. “WTO accession will lure foreign investors to telecom market”, the statement made by the Post and Telematics Minister in 2009. Vietnam's telecom and information technology sectors have many opportunities for development, especially in drawing foreign investment, after the country joined the WTO, that almost US$2 billion from foreign enterprises have been invested in telecom services.

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economic benefits and communicating convenience. The main mobile systems adopted include global system for mobile communications (GSM), general packet radio service (GPRS) and enhance data GSM environment (EDGE) system with 3 billion subscribers and 78 percent of market share. The first generation (1G) analog system is fast diminishing with only 1 million subscribers left in the advanced mobile phone system (AMPS), total access communication system (TACS) and Nordic mobile telephony 450/900 (NMT450/900). The second generation (2G) system is also decreasing and being replaced by the third generation (3G) services. The wideband code division multiple access (WCDMA) and high speed packet access network (HSPA) systems have 315 million subscribers with 8.2 percent of market share because of better service quality and download speeding.

Mobile telecommunications industry in the APEC shows pretty well performances both in penetration and growth of mobile subscribers. The APEC mobile penetration rate in 2008 was 90.46 percent, a 31 percent higher than the one in the global market. Although major APEC members have low growth rate in subscribers due to the saturated markets, the growth rate of 26.73% on average from 2003 to 2008 still surpassed the world’s value of 23.2%.

There are eight members in APEC which mobile penetration rate are fully saturated: Hong Kong, Singapore, Russia, Thailand, Taiwan, New Zealand, Australia and Malaysia with the respective penetration rates of 162.9, 138.15, 132.61, 118.04, 110.31, 109.22, 104.96 and 100.41 (ITU, 2009). In particular, Japan and Korea are the most well developed in the mobile service market, and their mobile broadband penetration rates were 56.8 and 48.58 in 2007, ranking in the world's top two. Japan, Korea, Taiwan, Hong Kong and Singapore are currently facing an issue in mobile services that their markets almost reach to the status of full saturation. As a result, these mobile operators focus on upgrading the mobile systems and the revenue growth in mobile data services. Given that the HSPA, a 3.5 generation service (3.5G), is of almost the full coverage in these countries, mobile operators and information and computer technology (ICT) companies work together to promote mobile data services with mobile Internet device (MID). For example, netbook3 boosts the demand for subscribers’ second phone number and stimulates the revenue in mobile data services.

The mobile penetration rate in the U.S. was 86.79 in 2008. In accordance with Forbes 2000 (2009), American Telephone & Telegraph (AT&T) and Verizon Communications are ranked as the first and third largest telecommunications operators in the world based on a mix of four metrics: sales, profit, assets and market value, indicating that the U.S. operators have determinable power in the global market. Mobile penetration rate in North America was about 75.65 percent in 2008. As compared with the markets in other APEC’s regions, the ratio of owing the second phone number is relatively low. Consequently, the strategies for these mobile operators are to increase wireless terminal connections for each user and to promote the demand of mobile broadband service.

There are two generations of commercial mobile service systems used in the APEC nowadays, including the 2G and the 3G. The GSM and cdmaOne are the two main systems in the 2G services. The 2G standard allows a maximum data rate of 9.6 kbps, which is possible to transmit voice and low volume digital data, for example, 3 A netbook is a laptop computer designed for wireless communication and access to the Internet. It ranges in size from below 5 inches to over 13, typically weighs 2 to 3 pounds and is often significantly cheaper than general purpose laptops.

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short message service (SMS) or multimedia message service (MMS). The WCDMA and CDMA2000 1X are the two main systems in the 3G services. The 3G standard increases the transmission rate up to 2 mega bit per second (Mbps), which is compatible with all mobile systems in the world and with the 2G networks. Due to its high data transmission rate, the 3G system is able to provide multimedia services, such as video transmission, video conferencing, and high-speed Internet access, and is widely applied to the other aspects of the daily life. Their extended versions (3.5G) are the HSPA and CDMA2000 1x EV-DO.

The major mobile system adopted in Asian markets is GSM, which accounted for the market share of 76.2 percent in 2008 (MIC, 2009). The other system technologies by subscriber share are cdmaOne and CDMA 2000 1X (12 percent), WCDMA and HSPA (7.4 percent), and CDMA2000 1x EV-DO (3.7 percent). SK Telecom and Korea Telecom Freetel (KTF) in Korea actively deploy WCDMA and HSDPA networks, as well as advocating the user to switch CDMA2000 1X system to WCDMA and HSDPA systems. So, the CDMA users in Asia are expected to slowly decrease in the future. The unique system, TD-SCDMA, offered by China Mobile in China, has grown in a tardy pace, because of its incomplete industry chain and communication quality. There were only 330,000 subscribers by the end of 2008. The main mobile system in North America markets is still the GSM, which accounted for the market share of 31.1 percent in 2008. The other system technologies by subscriber share are cdmaOne and CDMA 2000 1X (29.3 percent), and CDMA2000 1x EV-DO (21.2 percent) (MIC, 2009). The future technology developed by Verizon Wireless and Telecom Mobile(T-Mobile) in the U.S., and Telus and Bell in Canada are moving towards long term evolution (LTE), the fourth generation (4G) system.

Mobile service, being needed in our daily lives, has enormous impacts on world economy. Mobile services connect and communicate with people anytime and anywhere. The revenues of world mobile communication have steadily increased, reaching the total values of US 1,391 billion in 2008 (MIC, 2009). In 2007, global revenue (692 billion) of mobile service surpassed that (647 billion) of fixed-line service. Mobile service continually grows because of the newly developing markets and the various contents in 3G service. Undoubtedly, mobile service plays the mainstream role now and will do so in the future.

1.3 Research objective The purpose of this study is to measure the efficiency and productivity change of

28 mobile operators during the time period of 2003 to 2008, using the DEA and Malmquist index approaches. The operators are Telstra, Optus, CSL, NTT DoCoMo, KDDI, SK Telecom (SKT), KTF, Celcom, Telecom New Zealand, SingTel, Chunghwa Telecom (CHT), Taiwan Mobile (TMB), AIS, Total Access Communication (DTAC), Mobile TeleSystems (MTS), Vimpelcom, Verizon Wireless, AT&T Mobility, Telkomsel, Indosat, China Mobile, China Unicom, Smart Communication, Globe Telecom, Rogers Wireless, Bell Wireless, Telcel, Movistar. There are two output variables and three input variables adopted in this study. Two output variables are revenue and number of mobile subscribers, and three input variables are number of employees, total assets and capital expenditures, as commonly adopted in the literature.

2. Literature Review Most state-owned telecommunications operators worldwide experienced

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competitive changes through the deregulation and the privatization in the market. Traditional rate of return regulation was replaced with new price cap regulations. The digital convergence and liftoff of international investment restriction in telecommunications make the market fiercely competitive from all aspects. The productivity and efficiency are important for telecommunication operators. With the knowledge of the strength and weakness, the operators are able to modify their managerial strategies to increase the efficiency and to achieve higher profits. The issue of measuring productivity and efficiency of an industry is crucial to both the economic theorist and the economic policy maker (Farrell, 1957).

In the last two decades, there has been a growing interest in measuring the efficiency of telecommunications companies due to academic interest and to regulatory purposes. For example, Tsai, Chen and Tzeng (2006) adopted traditional DEA, Andersen and Petersen (A&P) efficiency measure and efficiency achievement measure to discover the productivity ranking of 39 leading telecommunication operators in Forbes 2000. The results indicated that Asia-Pacific telecom operators have better productivity efficiency than those in Europe and America. Lam and Shiu (2008) applied the DEA approach to measure the productivity performance of China’s telecommunications sector at the provincial level from 2003 to 2005. The results indicated that the efficiency scores for different provinces and regions are diverse. For instance, provinces and municipalities in the eastern region have achieved higher levels of technical efficiency than those in the central and western regions. Also, the differences in efficiency scores are mainly due to the differences in the operating environments of different provinces, rather than the efficiency performance of telecommunications enterprises. Yang and Chang (2009) used the DEA window analysis to examine the efficiency for Taiwan’s mobile firms between 2001 and 2005. The results showed that the acquisitions did help Taiwan Mobile and Far Eastone Telecom to improve their scale efficiencies but worsened pure technical efficiency in the short term. Also, Chunghwa Telecom did maintain its pure technical efficiency within a marginal variability, which implies that it might manage the resources in a more stable way. Finally, Liao and González (2009) applied partial factor productivity and the DEA to investigate the efficiency of mobile operators in BRICs (i.e., Brazil, Russia, India and China) during 2002 to 2006. They found that the two leading Brazilian mobile operators, Vivo and TIM, are fully efficient, but Indian mobile operators are the least efficient among BRICs operators.

Some researchers were interested in measuring the productivity growth to compare with competing operators. Lee, Park and Oh (2000) analyzed and compared the efficiency change of Korean Telecom (KT) before and after the introduction of both domestic and foreign competition by Partial productivity and Malmquist index methodology. The empirical results revealed that the overall efficiency of KT significantly improved due to the improvement of the allocative efficiency. The improvement of technical efficiency, however, was not significant due to hothouse competition and excessive regulation of government on corporate governance of KT. The study provided some insightful policy implications. Market condition needs to be more competitive, eliminating entry barriers and deregulating price regulation. The regulatory agency has to provide operators with the autonomy of management such as strategic marketing, diverse tariff and new services for consumer utility in order to accomplish the results of privatization and deregulation. Uri (2000, 2002) measure the productivity change of 19 local exchange carriers (LECs) in the United States and analyzed whether price cap, one popular incentive regulation plan, resulted in an

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increase in efficiency. Both studies used the same techniques, Malmquist index approach and conventional-growth accounting approach. However, the outcomes were somewhat different due to differences in the output variables and slight difference in the periods under study. Uri (2000) concluded that efficiency improved as a whole, but Uri (2002) indicated that in the aggregate there was virtually no change in efficiency. Incentive regulation was designed to promote efficiency. Thus, Uri (2000) suggested that the implementation of price cap was a success, while Uri (2002) implied that incentive regulation does not appear to have been successful. Further, Uri (2001) also measured the impact of price caps on productive efficiency, but used DEA methodology instead of Malmquist index approach. The results showed that there was no identifiable improvement in the aggregate LECs efficiency between 1988 and 1998. Calabrese, Campisi and Mancuso (2002) analyzed the evolution of labor and total factor productivity in the telecommunications industries of 13 OECD countries by using DEA , Malmquist TFP index and α, β convergence techniques. The paper also explored the existence of convergence in both labor and total factor productivity among the 13 telecom industries by means of a cross-section technique α and β-convergence. The studied revealed that two convergence tests implied no significant evidence. Finally, Lam and Lam (2005) adopted both the growth accounting approach and the Divisia aggregation method to estimate the total factor productivity (TFP) growth of the Hong Kong Telephone Company (HKTC) during 1964 to 1998. The TFP of HKTC was estimated to be from 2.31% to 3.56% per year in the study period.

The above studies of efficiency and productivity can be found that the DEA and Malmquist index approach were used more frequent than other methodologies for the evaluation of business performance. Unlike the SFA, the DEA and Malmquist approaches do not have to involve the detailed operational revenue/cost information and are feasible to be adopted in the current study. In particular, telecommunications operators are reluctant to publicize revenue/cost data due to the fierce competition in the market. As resulted, extensive studies obtained the needed data from the available published information such as the annual reports of operators and surveys of governments.

3. Research Methodology 3.1 Data envelopment analysis

The data envelopment analysis (DEA) approach is a non-parametric technique, which is based on linear programming, for measuring and evaluating the relative efficiencies of a set of entities with common inputs and outputs. It combines multiple outputs and inputs to construct a single measure of relative efficiency across similar organizational units, which are regarded as DMU. The characteristic of DEA is that it treats each DMU individually and estimates the weighs for the inputs and outputs that maximize the DMU's efficiency. It is unlike regression approaches in which the same weights are applied to all DMUs to produce one output measure; therefore, it can avoid the subjective deviations. Further, the advantage of DEA over other forms of production or cost efficiency measurement is that the prior assumption of the production function is not required while using DEA. The DEA can establish an efficiency frontier which consists of the efficient DMUs with the optimal levels of outputs for given levels of inputs, and evaluates DMU’s efficiency relative to the frontier. The DMU on the efficiency frontier is considered efficient if its outputs are optimal for its inputs in comparison with the inputs and outputs of all comparable DMUs. On contrast, the DMU placed inside the frontier is considered inefficient.

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DEA was first introduced by Charnes, Cooper and Rhodes (1978), known as the CCR model, as a generalization of efficiency proposed by Farrell (1957). We assume that there are n DMUs, and each DMU has m inputs to produce s output. This model measures the relative efficiency ratio of a given DMU (ho) by the sum of its weighted outputs to the sum of its weighted inputs. It can be formulated as follows, known as input-oriented CCR model:

1

1

maxs

r roro m

i ioi

u yh

v x=

=

= ∑∑

(1)

subject to

1

1

1s

r rjrm

i iji

u y

v x=

=

≤∑∑

,

, 0, 1, , , 1, , , 1, ,r iu v i m j n r s≥ = = = where ho is the efficiency ratio of the DMUo; vi, ur are virtual multipliers (weights) for the ith input and the rth output, respectively; m is the number of inputs, s is the number of outputs and n is the number of DMUs; xio is the value of the input i for DMUo, yro is the value of the output r for DMUo.

The equation (1) is fractional programming and has an infinite number of solutions. It can be solved by adding an additional constraint

11m

i ioiv x

==∑ . The form

then converts to the multiplier form of the DEA LP problem:

1max s

o r rorh ym

==∑ (2)

subject to

1 10, for 1, ,s m

r rj i ijr iy v x j nm

= =− ≤ =∑ ∑ ,

1

1mi ioi

v x=

=∑ ,

, 0, for 1, , 1, ,iv i m r sγm ε≥ > = = . To reflect the transformation, the variables from (u, v) has been replaced by (μ, ν).

ε is a non-Archimedean quantity defined to be smaller than any positive real number. The dual form of equation (2) can be written as an equivalent envelopment form as follows:

( )1 1min m s

o o ii rh s sγθ ε − +

= == − +∑ ∑ (3)

subject to

1for 1, ,n

ij j i iojx s x i mλ θ−

=+ = =∑ ,

1

for 1, ,nrj j roj

y s y r sγλ +=

− = =∑ ,

, , 0, >0, 1, ,j i rs s j nλ ε− + ≥ = . where θo is the proportion of DMUo’s inputs needed to produce a quantity of outputs equivalent to its benchmarked DMUs identified and weighted by the λj. si

- and sr+ are

the slack variables of input and output respectively. λj is a (n × 1) column vector of constants and can indicate benchmarked DMUs of DMUo. If ho

* = 1 is meant efficient and ho

* ＜ 1 is meant inefficient where the symbol “*” represents the optimal value.

However, the CCR model is calculated with the constant returns to scale (CRS)

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assumption. This assumption is not supportable in imperfectly competitive markets. The BCC model proposed by Banker, Charnes and Cooper (1984) modifies the CCR model by allowing variable returns to scale (VRS). The multiplier form of the BCC model can be formulated as follows:

1

max so r ro or

h y um=

= −∑ (4) subject to

1 1

0 for 1, ,s mj i ij oi

y v x u j nγ γγm

= =− − ≤ =∑ ∑

1

1mi ioi

v x=

=∑

, 0 1, , 1, , free in signi ov for i m r s uγm ε≥ > = = where uo is an indicator of returns to scale for BCC model. Increasing returns to scale for the DMUo if uo* ＜ 0, decreasing returns to scale if uo* ＞ 0 and constant returns to scale if uo* = 0. We can also obtain the dual BCC model by adding the constraint

11n

jjλ

==∑ , the dual form of equation (4) can be formulated as follows:

( )1 1min m s

o o i ri rh s sθ ε − +

= == − +∑ ∑ (5)

subject to

1for 1, ,n

ij j i iojx s x i mλ θ−

=+ = =∑ ,

+1

= for 1, ,nj j oj

y s y r sγ γ γλ=

− =∑

1

1njj

λ=

=∑ ,

, , 0, 0, for 1,j i rs s j nλ ε− + ≥ > = The Overall Technical Efficiency (OTE) from CCR model can be decomposed

into Pure Technical Efficiency (PTE) and Scale Efficiency (SE). The PTE can be obtained from BCC model. We can measure the SE for a DMUo by using CCR and BCC model as follow:

SE OTE PTE= (6) If the ratio is equal to 1 then a DMUo is scale efficient, otherwise if the ratio is less than one then a DMUo is scale inefficient.

Therefore, this study used the input-oriented CCR model and BCC model to obtain the above-mentioned values of efficiency. The input-oriented model measures how much less input might be saved to produce the same amount of output, and output-oriented model measures how much more output might be produced by using the same amount of input. This study considers the input-oriented because the outputs of the telecommunications industry may be driven by the market factors and competition, which beyond the control of the companies, whereas the companies may have a better control over the inputs.

3.2 Malmquist productivity index Malmquist index was first presented in consumer theory by Malmquist (1953),

who earlier constructed the quantity index as ratios of Shephard’s (1953) distance function in consumer theory context and later for productivity analysis by Caves, Christensen and Diewert (1982). Malmquist productivity index (MPI) presented by Färe et al. (1992) is used to distinguish between changes in efficiency (catch-up) and

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changes in the production frontier (technical change or innovation) under constant returns to scale (CRS) condition. In Färe, Grosskopf, Norris and Zhang (1994), the catch-up component can be further decomposed into pure technical efficiency change and scale efficiency change under variable returns to scale (VRS) condition. The Malmquist index can be used to measure the productivity growth and technical change in target achievement for an individual operational unit between periods as improved efficiency relative to the benchmark frontier.

The MPI is defined to use the distance functions, and consider in time period t that firms use inputs t nX R+∈ to produce outputs t mY R+∈ . The production

technology in period t may be defined as }{( , ), t tT X Y X can produce Y= .

According to Shephard (1970), the input/output distance function of a vector ( , )t tX Y is:

{ }0 ( , ) inf ( , / ) for 1,2,3,...,t t t t t tD X Y X Y T t Tθ θ= = ∈ =

The output distance function evaluates the ratio of tY , the maximum output under the fixed input tX and production technology tT . A value of one will be obtained from the distance function if Y is on the efficient frontier. Caves et al. (1982) defined the Malmquist index of productivity change between time period t (base year) and time period t+1 (final year), relative to the technology level at time period t:

( )( )

1 10

00

,

,

t t tt

t t t

D X YM

D X Y

+ +

=

Similarly, the Malmquist index of productivity change relative to technology at time t+1 can be defined as

( )( )

1 1 101

0 10

,

,

t t tt

t t t

D X YM

D X Y

+ + ++

+=

In order to avoid choosing an arbitrary benchmark, Färe et al. (1992) used the geometric mean of tM and 1tM + to represent the MPI

1 1

1 21 1 1 1 10 0

10 0

( , , , | )

( , | ) () , |( , | ) ( , | )

t t t to

t t t t t t

t t t t t t

M X Y X Y CRS

D X Y CRS D X Y CRSD X Y CRS D X Y CRS

+ +

+ + + + +

+

=

⋅

.

This index is the geometric mean of two input-based Malmquist TFP indices. (1) If 0M > 1, a positive Tfpch from period t to period t+1. (2) If 0M < 1, a negative Tfpch from period t to period t+1.

According to Färe et al. (1992), the Malmquist Tfpch index can be decomposed into technical change (Techch) and efficiency change (Effch), thus the equation can be rewritten as:

1 10

1 1 1 1 10 0 0

1 1 1 10 0 0

( , , , | )

( , | ) ( , | ) ( , | )( , | ) ( , | ) ( , | )

t t t t

t t t t t t t t t

t t t t t t t t t

M X Y X Y CRS

D X Y CRS D X Y CRS D X Y CRSD X Y CRS D X Y CRS D X Y CRS

+ +

+ + + + +

+ + + +

=

⋅

10

( )( )

1 1 10

0

,( )

,

t t t

t t t

D X Y CRSEffch CRS

D X Y CRS

+ + +

=

1 1

01 1 1 1

0 0

( , | ) ( , | )( )( , | ) ( , | )

t t t t t to

t t t t t t

D X Y CRS D X Y CRSTechch CRSD X Y CRS D X Y CRS

+ +

+ + + +

= ⋅

The term Effch, also known as the “catching up index”, measures the changes in relative position of a DMU to the production frontier between time period t and t+1 under CRS technology. Effch evaluates the efficiency of managerial manners or decisions (1) If Effch > 1, the managerial efficiency improved. (2) If Effch < 1, the managerial efficiency worsen.

The term Techch, also known as “frontier productivity index”, shows the relative distance between the frontiers and measures the change of frontiers between two periods. It is therefore sometimes referred to as the technical change effect. Techch measures the technical change of each DMU by calculating the geometric mean of the technical change from t to t+1 on different input invested. (1) If Techch > 1, the technology progressed. (2) If Techch < 1, the technology regressed. It is straightforward to relax the CRS assumption and assume VRS. Following Färe, Grosskopf and Lovell (1994), the efficiency change under CRS can be further decomposed into scale efficiency and pure technical efficiency under VRS.

( )( )

1 1 10

0

,( )

,

t t t

t t t

D X Y VRSPech VRS

D X Y VRS

+ + +

=

( ) ( )( ) ( )

1 1 1 1 1 10 0

0 0

, ,( )

, ,

t t t t t t

t t t t t t

D X Y CRS D X Y VRSSech VRS

D X Y CRS D X Y CRS

+ + + + + +

=

(1) If Pech(VRS) > 1, the efficiency improved.

(2) If Pech(VRS) < 1, the efficiency worsen.

(3) If Sech > 1, the DMU gets much closer to CRS, and its optimal productive scale size in long-term from period t to period t+1.

(4) If Sech < 1, the DMU gets much far away from CRS and its optimal productive scale size in long-term from period t to period t+1.

To sum up, the MPI can be decomposed into pure technical efficiency (Pech), scale efficiency (Sech) and technical change (Techch). Their relations are summarized as follow:

( )1 1, , , ( ) ( )

( ) ( )

t t t tiM Y X Y X Effch CRS Techch CRS

Pech VRS Sech Techch CRS

+ + = ×

= × × 3.3 Input and output variables

To examine an operator’s efficiency, many studies used total revenue (Pentzaropoulos and Giokas, 2002; Lam and Lam, 2005; Tsai, Chen and Tzeng, 2006;

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Liao and González, 2009) and number of calls or minute of calls (Uri, 2000, 2001 and 2002) as the output variables. Nevertheless, both number of calls and minute of calls are unavailable for most of the operators studied in the current analysis. Total revenues and subscribers are the most frequently used output variables in the related studies and they indicate the operating strengths and scales of an operator. Every mobile telecommunications operator needs sufficiently large amounts of revenues and subscribers to maintain its service operation of any scale. Subscribers of a mobile operator are the number of users who use its mobile services. Total revenues of an operator, defined as the operating revenues earned from the charge for these services, reflect the technology-variation characteristics of mobile operator and, in particular, the development of mobile market. However, not all of the operators would be willing to publish their detailed revenues due to the fierce competition in the market; hence, this study uses operating revenues (y1) and mobile subscribers (y2) as output variables instead. As for input variables, the number of employees (x1), total assets (x2) and capital expenditures (x3) are chosen in the study. Number of employees is referred to as the manpower employed by mobile operators or by the mobile segment of integrated business operators. It increases along with the operation scale of an operator and it is an important input for mobile service provision. Without an appropriate allocation of resources, redundant employees become burdens in operator’s expenditure. Total assets are defined as the summation of current assets, fixed assets, long-term investment, intangible assets and other investment in wireless segment. Capital expenditures are the total expenditures for the purchases of property, plant and equipment, intangible assets and other assets in one year of the wireless segment. Capital expenditures, used as investments, are fundamental to mobile communication industry and significantly affect call quality such as coverage of services, transmission speed, and network capacity. With more investments an operator can expand its system and improve its service, resulting high quality of services in turn attracts more subscribers and increases its revenues. Therefore, the number of employees (x1), total asset (x2) and capital expenditures (x3) are used as input variables in the DEA and Malmquist index.

4. Empirical Results 4.1 Data collection

The study analyzes 28 major mobile operators in APEC: Telstra and Optus in Australia; Bell Wireless and Rogers Wireless in Canada; China Mobile and China Unicom in China; CSL in Hong Kong; NTT DoCoMo and KDDI in Japan; SK Telecom and KT Freetel in Korea; Celcom in Malaysia; America Movil’s Telcel and Telefonica’s Movistar in Mexico; Telkomsel and Indosat in Indonesia; New Zealand Telecom in New Zealand; SingTel in Singapore; Smart Communications and Globe Telecom in Philippines; MTS and VimpelCom in Russia; Chunghwa Telecom (CHT) and Taiwan Mobile (TMB) in Taiwan; Advanced Info Service (AIS) and Total Access Communication (DTAC) in Thailand; Verizon Wireless and AT&T Mobility in the U.S.

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The operating and financial data was mainly obtained from these operators’ annual reports and the surveys released from telecommunications authorities and associations. The units of currencies of these data are transferred into US dollars by using the exchange rates announced by the Federal Reserve Bank of New York on the last business day of the fiscal years. It is noticeable that a fiscal year for the operators in Japan, Singapore and Optus in Australia ends on March 31 and for Telstra in Australia ends on June 30. Most importantly, in order to measure the efficiency of operators exclusively for mobile services, the data of integrated business operators which operate both fixed-line and mobile businesses used in this study were calculated by mobile revenue proportion of total telecommunications revenue.

4.2 Efficiency comparison In this section, the values of technical efficiency (TE) and pure technical

efficiency (PTE) are calculated. Then scale efficiency (SE), returns to scale and frequency of occurrence are obtained. The TE in the CCR model for each DMU can be decomposed into PTE and SE. Returns to scale address the input and output decisions of an operator. Constant return to scale (CRS) occurs when scale efficiency is equal to 1, which implies that operator’s production is under the optimal level and a proportionate increase in inputs increases output by the same proportion. A number of factors including, for example, imperfect competition and regulation, may cause suboptimal production.

If scale efficiency is less than one, there is scale inefficiency due to increasing return to scale or decreasing return to scale. When it is increasing returns to scale, operator should increase its input resource, such as raising number of employees and/or capital expenditures, to move into constant return to scale region; contrariwise in decreasing returns to scale. Frequency of occurrence refers to the frequencies with which fully efficient operators appear in the reference sets of the remaining mobile carriers. These fully efficient operators could be considered as the benchmarks and they are useful as good examples of efficiency improvement for inefficient ones.

The average efficiency for the APEC mobile operators during 2003-2008 is in Tables 1. First of all, three operators, Telkomsel, KDDI and Smart Communication, were fully efficient with all the values of TE, PTE and SE equal to 1 throughout the study period. This reveals that the usage of inputs and operating scale for these operators were well performed as compared to the mobile operators in APEC. The two economies, Indonesia and Philippines, have showed moderate developments in the last decade with the economic growths of 3.1% and 4.3% on average (The World Bank, 2009), even though they suffered from local political turbulence.4 However, Indonesian mobile market experienced a fast expanding phase during 2003 to 2008

4 Since the end of the New Order government in 1999, terrorism has become the most serious issue in Indonesia.

Many bombing attacks occurred during 2003 to nowadays. For example, blasts on the tourist island of Bali had killed 202 people, and a powerful bomb exploded near the Australian embassy in central Jakarta killing 10 Indonesians and wounding more than 100 in 2004. Besides, there were some independent movements, such as the free Aceh movement. They were a separatist group seeking independence for the Aceh region of Sumatra and fought against Indonesian government forces in the Aceh insurgency from 1976 to 2005, costing over 15,000 lives.

Terrorism in the Philippines is conflicts based on political issues conducted by rebel organizations against the Philippine government, its citizens and supporters. Most terrorism in the country is conducted by Islamic terrorist groups. There were some attacked activities. For example, “Davao international airport bombing”, a homemade bomb exploded at the Davao international airport killing at least 21 and wounding at least 146 in 2003 and “Valentine’s day bombings”, three bomb attacks took place in Makati city, killing up to 8 people and injuring dozens, possibly up to 150.

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and its penetration rate rose from 8.7% to 61.8%. Telkomsel had drastic increases in subscriber and revenue with the respective growth rates of 580% and 155%. Similarly, Smart Communication expanded its subscriber and revenue with the growth rates of 172 % and 70%, respectively. Contrarily to these two operators, KDDI only had moderate increases in asset, capital expenditure and employee by 34%, 131% and 36%, respectively. But, its revenue and subscriber increased by 70% and 74%, respectively. Hence, KDDI was identified as principal benchmarks within the current set of operators and had the highest frequency of occurrence in 2003, 2004, 2006 and 2007. To produce the same amount of output, these three operators used relatively few inputs because of the adoptions of efficient managerial strategies and resource allocation. Therefore, they were efficient for the six consecutive years.

In addition, Optus, KTF, China Unicom, SingTel, CHT and TWM demonstrated full efficiency in four or five years during 2003 to 2008. China Unicom was fully efficient during 2003 to 2007 and had the highest frequency of occurrence in 2004 and 2005. China Unicom, providing mobile services in most provinces in Mainland China, is the first NASDAQ-listed China telecommunications company that went public in 2004. Its operating performance was steadily well during 2003 to 2007 with the 78% increases both in revenue and subscriber. Its inputs of asset, capital expenditure and employee showed moderate increases with 17%, 108% and 70%, respectively. It is noticeable that, in 2008, its CDMA businesses were split and merged into China Telecom, resulting in a sharp decrease of 18% in subscriber. At the same time, because of its infrastructure investment in the WCDMA system of 3G service, asset increased by 60%, capital expenditure increased by 122%, and employee increased by 9.5%. Hence, technical efficiency of China Unicom in 2008 drastically deteriorated to 0.522. Next, KTF was identified as principal benchmarks in 2004, 2005, 2006, and 2008, and its efficiency scores were steadily high. The reason was that Korea and Japan pioneer global mobile markets with technology progress in CDMA2000 1x EV-DO and with versatile multimedia services.

On the other hand, Telstra, Rogers Wireless, Bell Wireless, Verizon Wireless and AT&T Mobility were the ones with the technical efficiency of less than 0.6 on average during 2003 to 2008. In particular, Telstra had the lowest efficiency of 0.531 on average. Telstra’s inputs of asset, capital expenditure and employee increased by 66.22%, 74.27% and 55.56%, but its revenue and subscriber only increased by 23.89% and 42.11%. Rogers Wireless and Bell Wireless, the largest two mobile operators in Canada, showed relatively low efficiency in operating performance. For instance, Roger Wireless’ inputs of asset, capital expenditure and employee grew by 183.98%, 138.07% and 136.99%, respectively. Similar cases applied to AT&T Mobility and Verizon Wireless in the U.S., in which AT&T Mobility’s inputs of asset, capital expenditure and employee grew by 339.34%, 296.87% and 88.57%, respectively.

In sum, these five less efficient operators all faced the same three market conditions: widespread territory with sparse population, market saturation and fierce competition. First; as a widespread territory, there are some possible reasons to drive operators operating inefficient. For example, the investment on a vast geographic market territory was costly. Also, the network upgrade and service operation were restricted in widespread territories with sparse population, making the rate of return on investment to be low. Second, full or close to full saturation did not provide enough incentive drives for the growth in revenue and subscriber. Mobile penetrations in Australia, Canada and the U.S. were 104.9%, 64.5% and 86.8% in 2008,

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respectively. Finally, fierce competition between operators also drove down the markup of mobile services. In addition, it induced a great pressure on lowering the tariffs but increasing the investment in employee input and system/equipment upgrade in order to maintain the cutting-edge advantage in the telecommunications market. Hence, there incurred a significant impact on service revenues of telecommunications operators. Consequently, the increase in service revenues driven by remarkable increase of mobile subscribers in recent years cannot be offset by the reduction in profit margin.

Table 1 Average efficiency for the APEC mobile operators during 2003-2008

Member DMU Technical efficiency

(CCR)

Pure technical efficiency

(BCC)

Scale efficiency

Frequency of

occurrence

Australia Telstra 0.531 0.534 0.994 0.000 Optus 0.946 0.968 0.977 4.667

Indonesia Telkomsel 1.000 1.000 1.000 7.333 Indosat 0.601 0.674 0.890 0.000

Hong Kong CSL 0.763 0.898 0.845 1.000

Japan NTT DoCoMo 0.870 1.000 0.870 0.000 KDDI 1.000 1.000 1.000 13.167

Korea SKT 0.939 0.969 0.970 2.000 KTF 1.000 1.000 1.000 3.167

Malaysia Celcom 0.613 0.648 0.946 0.000 New

Zealand Telecom New

Zealand 0.737 0.997 0.739 0.000

China China Mobile 0.660 1.000 0.660 0.000 China Unicom 0.920 0.981 0.932 9.000

Singapore SingTel 0.985 1.000 0.985 4.333

Taiwan CHT 0.963 0.965 0.997 3.667 TMB 0.991 1.000 0.991 3.667

Thailand AIS 0.835 0.858 0.973 1.000 DTAC 0.761 0.841 0.905 0.000

Philippines Smart

Communication 1.000 1.000 1.000 4.333

Globe Telecom 0.888 0.959 0.923 1.667

Russian MTS 0.836 0.855 0.978 0.000 Vimpelcom 0.723 0.732 0.988 0.000

Canada Rogers Wireless 0.568 0.607 0.940 0.000 Bell Wireless 0.593 0.648 0.927 0.000

Mexico Telcel 0.879 0.927 0.948 0.333 Movistar 0.687 0.825 0.826 0.000

U.S. Verizon Wireless 0.550 0.832 0.650 0.333 AT&T Mobility 0.584 0.927 0.636 0.000

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4.3 Productivity change comparison In this section, the changes in productivity of APEC mobile operators over the

period 2003-2008 are computed by the Malmquist index. The software adopted is the DEAP. The average values of technical change (Techch), efficiency change (Effch), pure efficiency change (Pech), scale change (Sech), and total factor productivity change (Tfpch) for each operator are reported in Table 2. The results of the analysis indicate that the productivity for all the operators increased by 5.5% on average (Tfpch = 1.055) during 2003 to 2008, equivalently about 1.1% per year. This growth is due primarily to improvements in efficiency (Effch = 1.055) rather than innovation (Techch = 1).

Of all the 28 operators in the APEC, 20 operators (Telstra, Optus, CSL, NTT DoCoMo, KDDI, SKT, Telecom New Zealand, SingTel, CHT, DTAC, Vimpelcom, AT&T Mobility, Telekomsel, Indosat, China Mobile, Smart Communication, Globe Telecom, Rogers Wireless, Bell Wireless and Movistar) were operating efficiently as measured by technical efficiency change relative to a constant return to scale technology during 2003 to 2008. Of these 20 operators, 3 operators (Telstra, Optus and CSL) displayed a constant technical efficiency change equal to 1. In contrast, the efficiency of 8 operators (KTF, Celcom, TMB, AIS, MTS, Verizon Wireless, China Unicom, Telcel) slightly declined. Smart Communication is the one of the highest efficiency change of 1.801 on average. This large improvement in technical efficiency by 80.1% was primarily driven by the 172% increase in subscriber during 2003 to 2008. Its marketing strategy of “Talk ‘N Text (TNT)” that offers unlimited calls within the network increased its subscriber base by 17.3% from 2007 to 2008.

Technical change (Techch) displayed a substantial variability among the APEC operators during 2003 to 2008, ranging from the value of 21.2 % (equivalently, 4.24 % annually for TMB) to that of -20.1 % (equivalently, -4.02 % annually for Globe Telecom). Much of this variability is a reflection of the types of service being provided, customer requirements, and competitive pressures in various market segments to innovate. Finally, Smart Communication had the highest productivity change of 1.918 during the study period (equivalently, 18.36% annually). But, Celcom is the operator with the worst productivity change of only 0.77.

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Table 2 Malmquist index of average annual productivity change for APEC mobile operators during the time period of 2003–2008

Member DMU Effch1 Techch2 Pech3 Sech4 Tfpch5

Australia Telstra 1.000 0.904 1.000 1.000 0.904 Optus 1.000 1.032 1.000 1.000 1.032

Indonesia Telkomsel 1.172 0.910 1.199 0.978 1.066 Indosat 1.488 0.939 1.325 1.123 1.398

Hong Kong CSL 1.000 1.041 1.000 1.000 1.041

Japan NTT DoCoMo 1.067 0.938 0.988 1.080 1.001 KDDI 1.152 0.902 1.150 1.002 1.039

Korea SKT 1.206 0.982 1.239 0.974 1.184 KTF 0.905 0.968 0.953 0.950 0.876

Malaysia Celcom 0.774 0.995 1.060 0.730 0.770 New

Zealand Telecom New

Zealand 1.145 1.119 1.000 1.145 1.281

China China Mobile 1.028 0.922 1.085 0.948 0.947 China Unicom 0.912 0.946 0.790 1.155 0.863

Singapore SingTel 1.078 0.844 1.000 1.078 0.910

Taiwan CHT 1.004 1.019 1.000 1.004 1.023 TMB 0.749 1.212 0.852 0.879 0.908

Thailand AIS 0.872 1.177 0.973 0.897 1.027 DTAC 1.116 0.941 1.000 1.116 1.051

Philippines Smart

Communication 1.801 1.065 1.277 1.410 1.918

Globe Telecom 1.036 0.799 1.024 1.012 0.827

Russian MTS 0.845 1.140 1.000 0.845 0.963 Vimpelcom 1.109 1.168 1.000 1.109 1.295

Canada Rogers Wireless 1.167 1.065 1.050 1.112 1.244 Bell Wireless 1.007 1.061 1.001 1.006 1.068

Mexico Telcel 0.966 0.853 0.919 1.051 0.824 Movistar 1.463 0.922 1.150 1.272 1.348

U.S.A. Verizon Wireless 0.990 1.122 1.000 0.990 1.111 AT&T Mobility 1.012 1.193 1.000 1.012 1.207

Average 1.055 1.000 1.031 1.023 1.055 Note: 1. “Effch” is technical efficiency change relative to constant returns to scale technology.

2. “Techch” is technological change. 3. “Pech” is pure technical efficiency change (i.e., relative to a variable returns to scale technology). 4. “Sech” is scale efficiency change. 5. “Tfpch” is the Malmquist index measuring total factor productivity (TFP) change.

5. Concluding Remarks The existing efficiency and productivity studies on telecommunications industry

mainly analyzed fixed-line operators or integrated operators (see, for example, Lee, Park and Oh, 2000; Uri, 2000 and 2002; Facanha and Resende, 2004; Lam and Lam, 2005; Tsai, Chen and Tzeng, 2006), but few focused on mobile operators or mobile sector of integrated operators. This study has analyzed the relative efficiency, productivity growth of 28 mobile operators in APEC over the time period of 2003 to

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2008 using the methodologies of DEA and Malmquist index. This study provides one contribution to the existing literature. There are sufficient DMUs used in this cross-country and cross-period study, i.e., 28 APEC mobile operators for the research period of six years, as compared to the related studies. A large number of 168 DMUs used in the analysis are to provide the results with higher discriminating power.5 The objects of telecommunications studies on efficiency measurement and productivity growth can approximately be divided into two categories: (1) single operator for a period of time and/or its regional operating centers at a particular time when experiencing different types of regulations, business reform and liberalization (Sueyoshi, 1998; Giokas and Pentzaropoulos, 2000; Lam and Lam, 2005); (2) multiple operators at a particular time and/or for a period of time when comparing them from international perspective or overall telecommunications industry of countries (Tsai, Chen and Tzeng, 2006; Lam and Shiu, 2008; Sastry, 2009; Yang and Chang, 2009). There are some limitations within this literature. The former one did not compare the object with other competing operators, and the latter one did not consider factors such as national development, mobile communication technology and application, market size, cultural, and usage habit of mobile services. Further, some studies compared mobile operators with integrated service operators (Tsai, Chen and Tzeng, 2006), and thus, the results might have possible bias.

The empirical results of this study can be summarized by the following two parts. In the DEA model, three operators, KDDI, Telkomsel and Smart Communication, were fully efficient with all the values of TE, PTE and SE equal to 1 throughout the study period. This result is supported by those in Tsai, Chen and Tzeng (2006) and Liao and Lin (2008), in which KDDI was also found to be efficient among leading telecom operators in Forbes 2000 in 2003 and among Japan’s and Korea’s markets during 2002 to 2006, respectively. On the other hand, Telstra, Rogers Wireless, Bell Wireless, Verizon Wireless and AT&T Mobility were the ones with the technical efficiency of less than 0.6 on average during 2003 to 2008. Similarly, Tsai, Chen and Tzeng (2006) found that AT&T Mobility and Bell Wireless were inefficient operators among leading telecom operators in Forbes 2000. Noticeable, Telstra was the least efficient operators in this study but performed fully efficiently in the study of Tsai, Chen and Tzeng (2006). The difference lied on that mobile segment of Telstra was analyzed in this study and its integrated services were analyzed in Tsai, Chen and Tzeng (2006).

In addition, operators in the market with vast geographic territory, such as MTS and Vimpelcom in Russia, Rogers Wireless and Bell Wireless in Canada, Verizon Wireless and AT&T Mobility in U.S. were usually inefficient. Labor redundancy and input misallocation were the main factors attributing efficiency deterioration. This study also finds that operators with large revenues do not necessarily achieve high efficiency. In particular, these operators, as the leading role in the telecommunication industry, have to develop pioneering technologies on services and applications and provide new network systems ahead of their rivals. Therefore, these actions might bring the inefficiency to large operators. This result is supported by those in Pentzaropoulos and Giokas (2002), Finnish operator, Sonera Telecom, was more efficient than British Telcom and France Telecom. For instance, the revenue scales of Verizon Wireless and AT&T Mobility are the largest among 28 mobile operators in 5 For example, an important experienced rule of thumb when using DEA, is that the number of DMUs is at least twice the sum of the number of inputs and that number plus outputs. Otherwise, the model may produce numerous relatively efficient units and decrease discriminating power.

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this study, but they revealed inefficient performance, which were only higher than Telstra.

In the Malmquist productivity index, the results showed that productivity increased by 5.5% between 2003 and 2008 or about 1.1% per year. This growth is due primarily to improvements in technical efficiency rather than innovation. This result is different from Uri (2002), in which overall productivity of 19 LECs in the United States increased primarily due to technology innovation. The reason may lie on the differences in variable selection in the analyses. Total revenues and subscribers were used as output variables in this study, and volumes of different services (e.g., local call, intrastate call and interstate call) were used as output variables in Uri (2002).

Further, of all the 28 operators in the study, 20 operators (Telstra, Optus, CSL, NTT DoCoMo, KDDI, SKT, Telecom New Zealand, SingTel, CHT, DTAC, Vimpelcom, AT&T Mobility, Telekomsel, Indosat, China Mobile, Smart Communication, Globe Telecom, Rogers Wireless, Bell Wireless and Movistar) were operating efficiently as measured by technical efficiency change relative to a constant return to scale technology during 2003 to 2008. Of these 20 operators, 3 operators (Telstra, Optus and CSL) displayed a constant efficiency change equal to 1. In contrast, the efficiency of remaining 8 operators (KTF, Celcom, TMB, AIS, MTS, Verizon Wireless, China Unicom and Telcel) slightly declined.

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